title: Fuzzy connectedness image segmentation for newborn brain extraction in MR images.
abstract: Newborn's brain has a various shape, and easily changes with not only brain developing and cerebral diseases. Although the brain segmentation in MR images is an effective way to quantify the brain shape and size, there are few studies in neonatal brain MR image analysis. This paper introduces a novel method based on fuzzy connectedness (FC) with fuzzy object model (FOM). FOM is built from a training dataset, and gives fuzzy degree belonging to parenchyma with respect to location and intensity. FC is calculated from object affinity and homogeneous affinity, and the object affinity is given by the FOM. The method first segments the white matter, and then segments the surrounding cortex. The propose method has been applied to 10 newborn subjects whose revised age was between -1 month and +2 month. Leave-on-out cross-validation (LOOCV) was conducted, and the mean false-positive volume fraction was 1.33%, the mean false-negative volume fraction was 2.90%, and geometric-mean was 1.42%.
authors.forename: Syoji
authors.initials: S
authors.lastname: Kobashi
authors.forename: Jayaram K
authors.initials: JK
authors.lastname: Udupa
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D017143:Fuzzy Logic; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging